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Diffstat (limited to 'site.cfg')
| -rwxr-xr-x | site.cfg | 157 |
1 files changed, 0 insertions, 157 deletions
diff --git a/site.cfg b/site.cfg deleted file mode 100755 index effb46f1102f..000000000000 --- a/site.cfg +++ /dev/null @@ -1,157 +0,0 @@ -# This file provides configuration information about non-Python dependencies for -# numpy.distutils-using packages. Create a file like this called "site.cfg" next -# to your package's setup.py file and fill in the appropriate sections. Not all -# packages will use all sections so you should leave out sections that your -# package does not use. - -# To assist automatic installation like easy_install, the user's home directory -# will also be checked for the file ~/.numpy-site.cfg . - -# The format of the file is that of the standard library's ConfigParser module. -# -# http://www.python.org/doc/current/lib/module-ConfigParser.html -# -# Each section defines settings that apply to one particular dependency. Some of -# the settings are general and apply to nearly any section and are defined here. -# Settings specific to a particular section will be defined near their section. -# -# libraries -# Comma-separated list of library names to add to compile the extension -# with. Note that these should be just the names, not the filenames. For -# example, the file "libfoo.so" would become simply "foo". -# libraries = lapack,f77blas,cblas,atlas -# -# library_dirs -# List of directories to add to the library search path when compiling -# extensions with this dependency. Use the character given by os.pathsep -# to separate the items in the list. Note that this character is known to -# vary on some unix-like systems; if a colon does not work, try a comma. -# This also applies to include_dirs and src_dirs (see below). -# On UN*X-type systems (OS X, most BSD and Linux systems): -# library_dirs = /usr/lib:/usr/local/lib -# On Windows: -# library_dirs = c:\mingw\lib,c:\atlas\lib -# On some BSD and Linux systems: -# library_dirs = /usr/lib,/usr/local/lib -# -# include_dirs -# List of directories to add to the header file earch path. -# include_dirs = /usr/include:/usr/local/include -# -# src_dirs -# List of directories that contain extracted source code for the -# dependency. For some dependencies, numpy.distutils will be able to build -# them from source if binaries cannot be found. The FORTRAN BLAS and -# LAPACK libraries are one example. However, most dependencies are more -# complicated and require actual installation that you need to do -# yourself. -# src_dirs = /home/rkern/src/BLAS_SRC:/home/rkern/src/LAPACK_SRC -# -# search_static_first -# Boolean (one of (0, false, no, off) for False or (1, true, yes, on) for -# True) to tell numpy.distutils to prefer static libraries (.a) over -# shared libraries (.so). It is turned off by default. -# search_static_first = false - -# Defaults -# ======== -# The settings given here will apply to all other sections if not overridden. -# This is a good place to add general library and include directories like -# /usr/local/{lib,include} -# -#[DEFAULT] -#library_dirs = /usr/local/lib -#include_dirs = /usr/local/include - -# Atlas -# ----- -# Atlas is an open source optimized implementation of the BLAS and Lapack -# routines. Numpy will try to build against Atlas by default when available in -# the system library dirs. To build numpy against a custom installation of -# Atlas you can add an explicit section such as the following. Here we assume -# that Atlas was configured with ``prefix=/opt/atlas``. -# -# [atlas] -# library_dirs = /opt/atlas/lib -# include_dirs = /opt/atlas/include - -# OpenBLAS -# -------- -# OpenBLAS is another open source optimized implementation of BLAS and Lapack -# and can be seen as an alternative to Atlas. To build numpy against OpenBLAS -# instead of Atlas, use this section instead of the above, adjusting as needed -# for your configuration (in the following example we installed OpenBLAS with -# ``make install PREFIX=/opt/OpenBLAS``. -# -# **Warning**: OpenBLAS, by default, is built in multithreaded mode. Due to the -# way Python's multiprocessing is implemented, a multithreaded OpenBLAS can -# cause programs using both to hang as soon as a worker process is forked on -# POSIX systems (Linux, Mac). -# This is fixed in Openblas 0.2.9 for the pthread build, the OpenMP build using -# GNU openmp is as of gcc-4.9 not fixed yet. -# Python 3.4 will introduce a new feature in multiprocessing, called the -# "forkserver", which solves this problem. For older versions, make sure -# OpenBLAS is built using pthreads or use Python threads instead of -# multiprocessing. -# (This problem does not exist with multithreaded ATLAS.) -# -# http://docs.python.org/3.4/library/multiprocessing.html#contexts-and-start-methods -# https://github.com/xianyi/OpenBLAS/issues/294 -# -[openblas] -libraries = openblas -library_dirs = /usr/lib -include_dirs = /usr/include - -# MKL -#---- -# MKL is Intel's very optimized yet proprietary implementation of BLAS and -# Lapack. -# For recent (9.0.21, for example) mkl, you need to change the names of the -# lapack library. Assuming you installed the mkl in /opt, for a 32 bits cpu: -# [mkl] -# library_dirs = /opt/intel/mkl/9.1.023/lib/32/ -# lapack_libs = mkl_lapack -# -# For 10.*, on 32 bits machines: -# [mkl] -# library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/ -# lapack_libs = mkl_lapack -# mkl_libs = mkl, guide - -# UMFPACK -# ------- -# The UMFPACK library is used in scikits.umfpack to factor large sparse matrices. -# It, in turn, depends on the AMD library for reordering the matrices for -# better performance. Note that the AMD library has nothing to do with AMD -# (Advanced Micro Devices), the CPU company. -# -# UMFPACK is not needed for numpy or scipy. -# -# http://www.cise.ufl.edu/research/sparse/umfpack/ -# http://www.cise.ufl.edu/research/sparse/amd/ -# http://scikits.appspot.com/umfpack -# -#[amd] -#amd_libs = amd -# -#[umfpack] -#umfpack_libs = umfpack - -# FFT libraries -# ------------- -# There are two FFT libraries that we can configure here: FFTW (2 and 3) and djbfft. -# Note that these libraries are not needed for numpy or scipy. -# -# http://fftw.org/ -# http://cr.yp.to/djbfft.html -# -# Given only this section, numpy.distutils will try to figure out which version -# of FFTW you are using. -#[fftw] -#libraries = fftw3 -# -# For djbfft, numpy.distutils will look for either djbfft.a or libdjbfft.a . -#[djbfft] -#include_dirs = /usr/local/djbfft/include -#library_dirs = /usr/local/djbfft/lib |